Adaptive scene-detection algorithm for VBR video stream
Adaptive scene-detection algorithm for VBR video stream
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DOI：
10.1109/tmm.2004.830812
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发表时间：
2004-08
影响因子：
7.3
通讯作者：
Hongliang Li;Guizhong Liu;Zhongwei Zhang;Yongli Li
中科院分区：
文献类型：
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作者：
Hongliang Li;Guizhong Liu;Zhongwei Zhang;Yongli Li
已经提出了几种仅基于比特率波动的场景检测算法。所有这些都是在固定的阈值上呈现的，这些阈值是通过视频特征的经验记录获得的。由于这些方法对记录的准确性的敏感性，这通常是通过重复测试几个值获得的，对于实际场景检测，特别是对于实时视频流量，可能会观察到不好的性能评估。本文回顾了这方面的研究成果，在帧级研究了场景持续时间和场景变化之间的相关性，同时研究了场景的局部统计特性，如方差和峰值比特率等。在此基础上，首先构造了一个有效的判决函数用于场景分割。然后，我们提出了一个场景检测算法，使用定义的动态阈值模型，它可以捕捉场景变化的统计特性。对15个可变码率MPEG视频序列的实验结果表明，该算法具有较好的场景检测性能，显著提高了场景检测精度。
Several scene-detection algorithms, which are only based on bit rate fluctuations, have been proposed. All of them are presented on the fixed thresholds, which are obtained by the empirical records of the video characteristics. Due to the sensitivity of these methods to the accuracy of the records, which are generally obtained by testing several values repeatedly, bad performance evaluation might be observed for the actual scene detection, especially for real-time video traffic. In this paper, we review the previous works in this area, and study the correlation between the scene duration and the scene change at the frame level, and simultaneously investigate the local statistical characteristics of scenes such as variance and peak bit rate etc. Based on this analysis, an effective decision function is first constructed for the scene segmentation. Then, we propose a scene-detection algorithm using the defined dynamic threshold model, which can capture the statistical properties of the scene changes. Experimental results using 15 variable bit rate MPEG video traces indicate good performances of the proposed algorithm with significantly improved scene-detection accuracy.